


'''simple docstring'''


import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile

from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError

import transformers
from transformers import (
    CONFIG_MAPPING,
    FEATURE_EXTRACTOR_MAPPING,
    PROCESSOR_MAPPING,
    TOKENIZER_MAPPING,
    AutoConfig,
    AutoFeatureExtractor,
    AutoProcessor,
    AutoTokenizer,
    BertTokenizer,
    ProcessorMixin,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available


sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))

from test_module.custom_configuration import CustomConfig  # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor  # noqa E402
from test_module.custom_processing import CustomProcessor  # noqa E402
from test_module.custom_tokenization import CustomTokenizer  # noqa E402


UpperCAmelCase_						:  Tuple				  =						get_tests_dir('fixtures/dummy_feature_extractor_config.json')
UpperCAmelCase_						:  Optional[int]				  =						get_tests_dir('fixtures/vocab.json')
UpperCAmelCase_						:  Optional[Any]				  =						get_tests_dir('fixtures')





class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							A_				: List[str]       =    ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= 0






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""    )
											self.assertIsInstance(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaConfig()
															_SCREAMING_SNAKE_CASE					:			List[str]							= AutoProcessor.from_pretrained("""facebook/wav2vec2-base-960h"""    )

															# save in new folder
															model_config.save_pretrained(__snake_case    )
															processor.save_pretrained(__snake_case    )

															_SCREAMING_SNAKE_CASE					:			Dict							= AutoProcessor.from_pretrained(__snake_case    )

											self.assertIsInstance(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											with tempfile.TemporaryDirectory() as tmpdirname:
															# copy relevant files
															copyfile(__snake_case       ,   os.path.join(__snake_case       ,   __snake_case    )    )
															copyfile(__snake_case       ,   os.path.join(__snake_case       ,   """vocab.json"""    )    )

															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= AutoProcessor.from_pretrained(__snake_case    )

											self.assertIsInstance(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE					:			Dict							= WavaVecaFeatureExtractor()
															_SCREAMING_SNAKE_CASE					:			str							= AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""    )

															_SCREAMING_SNAKE_CASE					:			Any							= WavaVecaProcessor(__snake_case       ,   __snake_case    )

															# save in new folder
															processor.save_pretrained(__snake_case    )

															# drop `processor_class` in tokenizer
															with open(os.path.join(__snake_case       ,   __snake_case    )       ,   """r"""    ) as f:
																			_SCREAMING_SNAKE_CASE					:			Dict							= json.load(__snake_case    )
																			config_dict.pop("""processor_class"""    )

															with open(os.path.join(__snake_case       ,   __snake_case    )       ,   """w"""    ) as f:
																			f.write(json.dumps(__snake_case    )    )

															_SCREAMING_SNAKE_CASE					:			Optional[int]							= AutoProcessor.from_pretrained(__snake_case    )

											self.assertIsInstance(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaFeatureExtractor()
															_SCREAMING_SNAKE_CASE					:			str							= AutoTokenizer.from_pretrained("""facebook/wav2vec2-base-960h"""    )

															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= WavaVecaProcessor(__snake_case       ,   __snake_case    )

															# save in new folder
															processor.save_pretrained(__snake_case    )

															# drop `processor_class` in feature extractor
															with open(os.path.join(__snake_case       ,   __snake_case    )       ,   """r"""    ) as f:
																			_SCREAMING_SNAKE_CASE					:			Dict							= json.load(__snake_case    )
																			config_dict.pop("""processor_class"""    )

															with open(os.path.join(__snake_case       ,   __snake_case    )       ,   """w"""    ) as f:
																			f.write(json.dumps(__snake_case    )    )

															_SCREAMING_SNAKE_CASE					:			str							= AutoProcessor.from_pretrained(__snake_case    )

											self.assertIsInstance(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											with tempfile.TemporaryDirectory() as tmpdirname:
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= WavaVecaConfig(processor_class="""Wav2Vec2Processor"""    )
															model_config.save_pretrained(__snake_case    )
															# copy relevant files
															copyfile(__snake_case       ,   os.path.join(__snake_case       ,   """vocab.json"""    )    )
															# create emtpy sample processor
															with open(os.path.join(__snake_case       ,   __snake_case    )       ,   """w"""    ) as f:
																			f.write("""{}"""    )

															_SCREAMING_SNAKE_CASE					:			int							= AutoProcessor.from_pretrained(__snake_case    )

											self.assertIsInstance(__snake_case       ,   __snake_case    )






							def UpperCAmelCase_      (       self    ):
											# If remote code is not set, we will time out when asking whether to load the model.
											with self.assertRaises(__snake_case    ):
															_SCREAMING_SNAKE_CASE					:			str							= AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""    )
											# If remote code is disabled, we can't load this config.
											with self.assertRaises(__snake_case    ):
															_SCREAMING_SNAKE_CASE					:			Optional[int]							= AutoProcessor.from_pretrained(
															    """hf-internal-testing/test_dynamic_processor"""       ,   trust_remote_code=__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Dict							= AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""       ,   trust_remote_code=__snake_case    )
											self.assertTrue(processor.special_attribute_present    )
											self.assertEqual(processor.__class__.__name__       ,   """NewProcessor"""    )

											_SCREAMING_SNAKE_CASE					:			Optional[int]							= processor.feature_extractor
											self.assertTrue(feature_extractor.special_attribute_present    )
											self.assertEqual(feature_extractor.__class__.__name__       ,   """NewFeatureExtractor"""    )

											_SCREAMING_SNAKE_CASE					:			Any							= processor.tokenizer
											self.assertTrue(tokenizer.special_attribute_present    )
											if is_tokenizers_available():
															self.assertEqual(tokenizer.__class__.__name__       ,   """NewTokenizerFast"""    )

															# Test we can also load the slow version
															_SCREAMING_SNAKE_CASE					:			List[Any]							= AutoProcessor.from_pretrained(
															    """hf-internal-testing/test_dynamic_processor"""       ,   trust_remote_code=__snake_case       ,   use_fast=__snake_case    )
															_SCREAMING_SNAKE_CASE					:			Tuple							= new_processor.tokenizer
															self.assertTrue(new_tokenizer.special_attribute_present    )
															self.assertEqual(new_tokenizer.__class__.__name__       ,   """NewTokenizer"""    )
											else:
															self.assertEqual(tokenizer.__class__.__name__       ,   """NewTokenizer"""    )






							def UpperCAmelCase_      (       self    ):
											try:
															AutoConfig.register("""custom"""       ,   __snake_case    )
															AutoFeatureExtractor.register(__snake_case       ,   __snake_case    )
															AutoTokenizer.register(__snake_case       ,   slow_tokenizer_class=__snake_case    )
															AutoProcessor.register(__snake_case       ,   __snake_case    )
															# Trying to register something existing in the Transformers library will raise an error
															with self.assertRaises(__snake_case    ):
																			AutoProcessor.register(__snake_case       ,   __snake_case    )

															# Now that the config is registered, it can be used as any other config with the auto-API
															_SCREAMING_SNAKE_CASE					:			Dict							= CustomFeatureExtractor.from_pretrained(__snake_case    )

															with tempfile.TemporaryDirectory() as tmp_dir:
																			_SCREAMING_SNAKE_CASE					:			Dict							= os.path.join(__snake_case       ,   """vocab.txt"""    )
																			with open(__snake_case       ,   """w"""       ,   encoding="""utf-8"""    ) as vocab_writer:
																							vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]    )    )
																			_SCREAMING_SNAKE_CASE					:			List[str]							= CustomTokenizer(__snake_case    )

															_SCREAMING_SNAKE_CASE					:			int							= CustomProcessor(__snake_case       ,   __snake_case    )

															with tempfile.TemporaryDirectory() as tmp_dir:
																			processor.save_pretrained(__snake_case    )
																			_SCREAMING_SNAKE_CASE					:			str							= AutoProcessor.from_pretrained(__snake_case    )
																			self.assertIsInstance(__snake_case       ,   __snake_case    )

											finally:
															if "custom" in CONFIG_MAPPING._extra_content:
																			del CONFIG_MAPPING._extra_content["custom"]
															if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																			del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
															if CustomConfig in TOKENIZER_MAPPING._extra_content:
																			del TOKENIZER_MAPPING._extra_content[CustomConfig]
															if CustomConfig in PROCESSOR_MAPPING._extra_content:
																			del PROCESSOR_MAPPING._extra_content[CustomConfig]






							def UpperCAmelCase_      (       self    ):





											class        lowercase__    (		_snake_case   ):





																		'''simple docstring'''


																		A_				: int       =    False





											class        lowercase__    (		_snake_case   ):





																		'''simple docstring'''


																		A_				: Any       =    False





											class        lowercase__    (		_snake_case   ):





																		'''simple docstring'''


																		A_				: Optional[Any]       =    """AutoFeatureExtractor"""
																		A_				: List[Any]       =    """AutoTokenizer"""
																		A_				: Optional[Any]       =    False

											try:
															AutoConfig.register("""custom"""       ,   __snake_case    )
															AutoFeatureExtractor.register(__snake_case       ,   __snake_case    )
															AutoTokenizer.register(__snake_case       ,   slow_tokenizer_class=__snake_case    )
															AutoProcessor.register(__snake_case       ,   __snake_case    )
															# If remote code is not set, the default is to use local classes.
															_SCREAMING_SNAKE_CASE					:			int							= AutoProcessor.from_pretrained("""hf-internal-testing/test_dynamic_processor"""    )
															self.assertEqual(processor.__class__.__name__       ,   """NewProcessor"""    )
															self.assertFalse(processor.special_attribute_present    )
															self.assertFalse(processor.feature_extractor.special_attribute_present    )
															self.assertFalse(processor.tokenizer.special_attribute_present    )

															# If remote code is disabled, we load the local ones.
															_SCREAMING_SNAKE_CASE					:			Tuple							= AutoProcessor.from_pretrained(
															    """hf-internal-testing/test_dynamic_processor"""       ,   trust_remote_code=__snake_case    )
															self.assertEqual(processor.__class__.__name__       ,   """NewProcessor"""    )
															self.assertFalse(processor.special_attribute_present    )
															self.assertFalse(processor.feature_extractor.special_attribute_present    )
															self.assertFalse(processor.tokenizer.special_attribute_present    )

															# If remote is enabled, we load from the Hub.
															_SCREAMING_SNAKE_CASE					:			Dict							= AutoProcessor.from_pretrained(
															    """hf-internal-testing/test_dynamic_processor"""       ,   trust_remote_code=__snake_case    )
															self.assertEqual(processor.__class__.__name__       ,   """NewProcessor"""    )
															self.assertTrue(processor.special_attribute_present    )
															self.assertTrue(processor.feature_extractor.special_attribute_present    )
															self.assertTrue(processor.tokenizer.special_attribute_present    )

											finally:
															if "custom" in CONFIG_MAPPING._extra_content:
																			del CONFIG_MAPPING._extra_content["custom"]
															if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
																			del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
															if CustomConfig in TOKENIZER_MAPPING._extra_content:
																			del TOKENIZER_MAPPING._extra_content[CustomConfig]
															if CustomConfig in PROCESSOR_MAPPING._extra_content:
																			del PROCESSOR_MAPPING._extra_content[CustomConfig]






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Any							= AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-bert"""    )
											self.assertEqual(processor.__class__.__name__       ,   """BertTokenizerFast"""    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Dict							= AutoProcessor.from_pretrained("""hf-internal-testing/tiny-random-convnext"""    )
											self.assertEqual(processor.__class__.__name__       ,   """ConvNextImageProcessor"""    )





@is_staging_test
class        lowercase__    (		unittest.TestCase   ):





							'''simple docstring'''


							A_				: List[str]       =    ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]






							@classmethod
							def UpperCAmelCase_      (       cls    ):
											_SCREAMING_SNAKE_CASE					:			Any							= TOKEN
											HfFolder.save_token(__snake_case    )






							@classmethod
							def UpperCAmelCase_      (       cls    ):
											try:
															delete_repo(token=cls._token       ,   repo_id="""test-processor"""    )
											except HTTPError:
															pass

											try:
															delete_repo(token=cls._token       ,   repo_id="""valid_org/test-processor-org"""    )
											except HTTPError:
															pass

											try:
															delete_repo(token=cls._token       ,   repo_id="""test-dynamic-processor"""    )
											except HTTPError:
															pass






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= WavaVecaProcessor.from_pretrained(__snake_case    )
											with tempfile.TemporaryDirectory() as tmp_dir:
															processor.save_pretrained(
															    os.path.join(__snake_case       ,   """test-processor"""    )       ,   push_to_hub=__snake_case       ,   use_auth_token=self._token    )

															_SCREAMING_SNAKE_CASE					:			List[Any]							= WavaVecaProcessor.from_pretrained(f"""{USER}/test-processor"""    )
															for k, v in processor.feature_extractor.__dict__.items():
																			self.assertEqual(__snake_case       ,   getattr(new_processor.feature_extractor       ,   __snake_case    )    )
															self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,   processor.tokenizer.get_vocab()    )






							def UpperCAmelCase_      (       self    ):
											_SCREAMING_SNAKE_CASE					:			str							= WavaVecaProcessor.from_pretrained(__snake_case    )

											with tempfile.TemporaryDirectory() as tmp_dir:
															processor.save_pretrained(
															    os.path.join(__snake_case       ,   """test-processor-org"""    )       ,   push_to_hub=__snake_case       ,   use_auth_token=self._token       ,   organization="""valid_org"""       ,   )

															_SCREAMING_SNAKE_CASE					:			Any							= WavaVecaProcessor.from_pretrained("""valid_org/test-processor-org"""    )
															for k, v in processor.feature_extractor.__dict__.items():
																			self.assertEqual(__snake_case       ,   getattr(new_processor.feature_extractor       ,   __snake_case    )    )
															self.assertDictEqual(new_processor.tokenizer.get_vocab()       ,   processor.tokenizer.get_vocab()    )






							def UpperCAmelCase_      (       self    ):
											CustomFeatureExtractor.register_for_auto_class()
											CustomTokenizer.register_for_auto_class()
											CustomProcessor.register_for_auto_class()

											_SCREAMING_SNAKE_CASE					:			List[str]							= CustomFeatureExtractor.from_pretrained(__snake_case    )

											with tempfile.TemporaryDirectory() as tmp_dir:
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= os.path.join(__snake_case       ,   """vocab.txt"""    )
															with open(__snake_case       ,   """w"""       ,   encoding="""utf-8"""    ) as vocab_writer:
																			vocab_writer.write("""""".join([x + """\n""" for x in self.vocab_tokens]    )    )
															_SCREAMING_SNAKE_CASE					:			List[str]							= CustomTokenizer(__snake_case    )

											_SCREAMING_SNAKE_CASE					:			Tuple							= CustomProcessor(__snake_case       ,   __snake_case    )

											with tempfile.TemporaryDirectory() as tmp_dir:
															create_repo(f"""{USER}/test-dynamic-processor"""       ,   token=self._token    )
															_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= Repository(__snake_case       ,   clone_from=f"""{USER}/test-dynamic-processor"""       ,   token=self._token    )
															processor.save_pretrained(__snake_case    )

															# This has added the proper auto_map field to the feature extractor config
															self.assertDictEqual(
															    processor.feature_extractor.auto_map       ,   {
															        """AutoFeatureExtractor""": """custom_feature_extraction.CustomFeatureExtractor""",
															        """AutoProcessor""": """custom_processing.CustomProcessor""",
															    }       ,   )

															# This has added the proper auto_map field to the tokenizer config
															with open(os.path.join(__snake_case       ,   """tokenizer_config.json"""    )    ) as f:
																			_SCREAMING_SNAKE_CASE					:			str							= json.load(__snake_case    )
															self.assertDictEqual(
															    tokenizer_config["""auto_map"""]       ,   {
															        """AutoTokenizer""": ["""custom_tokenization.CustomTokenizer""", None],
															        """AutoProcessor""": """custom_processing.CustomProcessor""",
															    }       ,   )

															# The code has been copied from fixtures
															self.assertTrue(os.path.isfile(os.path.join(__snake_case       ,   """custom_feature_extraction.py"""    )    )    )
															self.assertTrue(os.path.isfile(os.path.join(__snake_case       ,   """custom_tokenization.py"""    )    )    )
															self.assertTrue(os.path.isfile(os.path.join(__snake_case       ,   """custom_processing.py"""    )    )    )

															repo.push_to_hub()

											_SCREAMING_SNAKE_CASE					:			List[Any]							= AutoProcessor.from_pretrained(f"""{USER}/test-dynamic-processor"""       ,   trust_remote_code=__snake_case    )
											# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
											self.assertEqual(new_processor.__class__.__name__       ,   """CustomProcessor"""    )








'''simple docstring'''


import numpy as np





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self    ):
											_SCREAMING_SNAKE_CASE					:			List[Any]							= (0, 0)
											_SCREAMING_SNAKE_CASE					:			List[str]							= None
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0
											_SCREAMING_SNAKE_CASE					:			List[str]							= 0
											_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= 0






							def __eq__(       self       ,   __snake_case    ):
											return self.position == cell.position






							def UpperCAmelCase_      (       self    ):
											print(self.position    )





class        lowercase__    :





							'''simple docstring'''


							def __init__(       self       ,   __snake_case=(5, 5)    ):
											_SCREAMING_SNAKE_CASE					:			str							= np.zeros(__snake_case    )
											_SCREAMING_SNAKE_CASE					:			Optional[Any]							= world_size[0]
											_SCREAMING_SNAKE_CASE					:			Tuple							= world_size[1]






							def UpperCAmelCase_      (       self    ):
											print(self.w    )






							def UpperCAmelCase_      (       self       ,   __snake_case    ):
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= [
											    (-1, -1),
											    (-1, 0),
											    (-1, 1),
											    (0, -1),
											    (0, 1),
											    (1, -1),
											    (1, 0),
											    (1, 1),
											]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[0]
											_SCREAMING_SNAKE_CASE					:			Optional[int]							= cell.position[1]
											_SCREAMING_SNAKE_CASE					:			Tuple							= []
											for n in neughbour_cord:
															_SCREAMING_SNAKE_CASE					:			Optional[Any]							= current_x + n[0]
															_SCREAMING_SNAKE_CASE					:			List[Any]							= current_y + n[1]
															if 0 <= x < self.world_x_limit and 0 <= y < self.world_y_limit:
																			_SCREAMING_SNAKE_CASE					:			Optional[int]							= Cell()
																			_SCREAMING_SNAKE_CASE					:			Union[str, Any]							= (x, y)
																			_SCREAMING_SNAKE_CASE					:			Dict							= cell
																			neighbours.append(__snake_case    )
											return neighbours







def      snake_case_  (    SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__				, SCREAMING_SNAKE_CASE__	):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE					:			Tuple							= []
				_SCREAMING_SNAKE_CASE					:			Optional[Any]							= []
				_open.append(SCREAMING_SNAKE_CASE__	)

				while _open:
								_SCREAMING_SNAKE_CASE					:			Any							= np.argmin([n.f for n in _open]	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= _open[min_f]
								_closed.append(_open.pop(SCREAMING_SNAKE_CASE__	)	)
								if current == goal:
												break
								for n in world.get_neigbours(SCREAMING_SNAKE_CASE__	):
												for c in _closed:
																if c == n:
																				continue
												_SCREAMING_SNAKE_CASE					:			Optional[int]							= current.g + 1
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[Any]							= n.position
												_SCREAMING_SNAKE_CASE						,			_SCREAMING_SNAKE_CASE					:			List[str]							= goal.position
												_SCREAMING_SNAKE_CASE					:			Dict							= (ya - ya) ** 2 + (xa - xa) ** 2
												_SCREAMING_SNAKE_CASE					:			Any							= n.h + n.g

												for c in _open:
																if c == n and c.f < n.f:
																				continue
												_open.append(SCREAMING_SNAKE_CASE__	)
				_SCREAMING_SNAKE_CASE					:			Any							= []
				while current.parent is not None:
								path.append(current.position	)
								_SCREAMING_SNAKE_CASE					:			List[Any]							= current.parent
				path.append(current.position	)
				return path[::-1]


if __name__ == "__main__":
				UpperCAmelCase_						:  List[Any]				  =						Gridworld()
				# Start position and goal
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(0, 0)
				UpperCAmelCase_						:  Optional[Any]				  =						Cell()
				UpperCAmelCase_						:  Tuple				  =						(4, 4)
				print(F"path from {start.position} to {goal.position}")
				UpperCAmelCase_						:  Union[str, Any]				  =						astar(world, start, goal)
				#   Just for visual reasons.
				for i in s:
								UpperCAmelCase_						:  Optional[int]				  =						1
				print(world.w)




